Jacob Schreiber

`model.probability` will give you P(sequence|model). Using Bayes rule you can just calculate `model.probability` for each HMM and choose the one with the highest probability.

> Thanks for your reply @jmschrei ! In order to avoid confusion: I want to calculate P(sequence|model) , but the sequence is not the symbols itself (not a state symbol),...

I'm not sure if you're saying that the selected states from `model.predict` don't match the highest probabilities from `model.predict_proba`, or if the highest probability states in `model.predict_proba` don't match the...

Sorry that you're encountering issues. I'm currently rewriting pomegranate from the ground up using PyTorch, so all these installation issues should be resolved then.

Howdy @Ebedthan and @rjorton! Thanks for agreeing to review this submission. The process for conducting a review is outlined above. Please run the command shown above to have @editorialbot generate...

Hi @FarmOmics. Yes, Avocado can be applied to any compendium of bulk genomic experiments. However, you need many experiments across tissues and assays for Avocado to be accurate. I don't...

The way that Avocado is set up is that it can make predictions, even across species, for any assay that is measured at least once and any cell type that...

I think I figured it out. I can use the {celltype}_Regions.bed file to line everything up. I have another question about that, though. In Additional File 1 of "Genome-wide prediction...

The full data set would be great. All I need is the region file and the 200 bp data file. Also to confirm, is the data in the data files...

Good question! This is exactly what BayesianNetwork.from_structure does. You pass in the structure as a set of tuples and all it does is learn the parameters of the tables. On...